Pulmonary diseases such as bronchiectasis, asthma, cystic fibrosis and chronic obstructive pulmonary disease (COPD) are characterized by abnormalities in airway dimensions, including the thickness of the walls and the size of the lumen (i.e., the inner airway). Computed tomography (CT) has become one of the primary means to depict and detect these abnormalities since the availability of high-resolution, near-isotropic data makes it possible to evaluate airways that are at oblique angles to the scanner plane. However, at present, clinical evaluation of the airway tree is typically limited to subjective visual inspection only. Systematic evaluations of the airways, which would take advantage of high-resolution data, have not proved practical without substantial automation.
Also, many patients with airway disease are followed after an initial assessment in order to monitor disease progression or treatment response. In this situation, a prior and a follow-up dataset may be compared side-by-side in order to determine whether the patient's airway abnormalities are increasing or decreasing. However, this task is even more difficult to perform rigorously than the evaluation of a single time-point dataset since multiple airways must be visually compared. A method for systematic evaluation of anatomical changes resulting from treatment would be highly valuable for evaluating patients undergoing treatments. If a treatment is ineffective, it would be highly beneficial to determine this quickly and objectively in order to discontinue the course of treatment and its side effects. Furthermore, a systematic evaluation would benefit researchers developing new treatments to rapidly determine which ones show promise.
In many instances, visualization of change provides benefits over simple, numeric measurements, especially in radiology where physicians and health professionals are trained to rely on visual assessments.